Evaluation of RISK survival models

This document highlights the use of

for the evaluation (RRPlot), and calibration of cox models (CoxRiskCalibration) or logistic models (CalibrationProbPoissonRisk) of survival data.

Furthermore, it can be used to evaluate any Risk index that reruns the probability of a future event on external data-set.

This document will use the survival::rotterdam, and survival::gbsg data-sets to train and predict the risk of cancer recurrence after surgery. Both Cox and Logistic models will be trained and evaluated.

Here are some sample plots returned by the evaluated functions:

The libraries

library(survival)
library(FRESA.CAD)
## Loading required package: Rcpp
## Loading required package: stringr
## Loading required package: miscTools
## Loading required package: Hmisc
## 
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
## 
##     format.pval, units
## Loading required package: pROC
## Type 'citation("pROC")' for a citation.
## 
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var
source("~/GitHub/FRESA.CAD/R/RRPlot.R")
source("~/GitHub/FRESA.CAD/R/PoissonEventRiskCalibration.R")
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)

Breast Cancer Royston-Altman data

data(gbsg, package=“survival”) and data(rotterdam, package=“survival”)

gbsgdata <- gbsg
rownames(gbsgdata) <- gbsgdata$pid
gbsgdata$pid <- NULL

odata <-rotterdam
rownames(odata) <- odata$pid
odata$pid <- NULL
odata$rfstime <- odata$rtime
odata$status <- odata$recur
odata$rtime <- NULL
odata$recur <- NULL

odata <- odata[,colnames(odata) %in% colnames(gbsgdata)]

odata$size <- 10*(odata$size=="<=20") + 
  35*(odata$size=="20-50") + 
  60*(odata$size==">50")

data <- as.data.frame(model.matrix(Surv(rfstime,status)~.*.,odata))

data$`(Intercept)` <- NULL

dataBrestCancerTrain <- cbind(time=odata[rownames(data),"rfstime"],status=odata[rownames(data),"status"],data)

colnames(dataBrestCancerTrain) <-str_replace_all(colnames(dataBrestCancerTrain),":","_")
colnames(dataBrestCancerTrain) <-str_replace_all(colnames(dataBrestCancerTrain)," ","")
colnames(dataBrestCancerTrain) <-str_replace_all(colnames(dataBrestCancerTrain),"\\.","_")
colnames(dataBrestCancerTrain) <-str_replace_all(colnames(dataBrestCancerTrain),"-","_")
colnames(dataBrestCancerTrain) <-str_replace_all(colnames(dataBrestCancerTrain),">","_")
dataBrestCancerTrain$time <- dataBrestCancerTrain$time/365 ## To years


pander::pander(table(odata[rownames(data),"status"]),caption="rotterdam")
rotterdam
0 1
1464 1518

data(gbsg, package=“survival”) data conditioning

gbsgdata <- gbsgdata[,colnames(odata)]
data <- as.data.frame(model.matrix(Surv(rfstime,status)~.*.,gbsgdata))

data$`(Intercept)` <- NULL

dataBrestCancerTest <- cbind(time=gbsgdata[rownames(data),"rfstime"],status=gbsgdata[rownames(data),"status"],data)

colnames(dataBrestCancerTest) <-str_replace_all(colnames(dataBrestCancerTest),":","_")
colnames(dataBrestCancerTest) <-str_replace_all(colnames(dataBrestCancerTest)," ","")
colnames(dataBrestCancerTest) <-str_replace_all(colnames(dataBrestCancerTest),"\\.","_")
colnames(dataBrestCancerTest) <-str_replace_all(colnames(dataBrestCancerTest),"-","_")
colnames(dataBrestCancerTest) <-str_replace_all(colnames(dataBrestCancerTest),">","_")
dataBrestCancerTest$time <- dataBrestCancerTest$time/365

pander::pander(table(odata[rownames(data),"status"]), caption="gbsg")
gbsg
0 1
499 183

Cox Modeling

ml <- BSWiMS.model(Surv(time,status)~.,data=dataBrestCancerTrain,loops=0)
sm <- summary(ml)
pander::pander(sm$coefficients)
  Estimate lower mean upper u.Accuracy r.Accuracy full.Accuracy u.AUC r.AUC full.AUC IDI NRI z.IDI z.NRI Delta.AUC Frequency
size_nodes -8.55e-04 -0.001390 -0.000883 -0.000303 0.624 0.644 0.646 0.629 0.645 0.647 3.27e-03 0.33343 7.01 9.3054 -0.00172 1
nodes 1.88e-01 0.099011 0.186403 0.302745 0.637 0.648 0.646 0.640 0.649 0.647 3.84e-03 0.13951 5.59 3.8202 0.00260 1
grade 5.39e-01 0.347964 0.549395 0.765913 0.565 0.645 0.646 0.561 0.646 0.647 5.54e-03 0.11567 4.97 3.6766 -0.00104 1
age -7.42e-03 -0.012459 -0.007599 -0.002897 0.513 0.629 0.646 0.513 0.629 0.647 3.48e-03 0.09715 4.90 2.6559 -0.01764 1
grade_nodes -2.91e-02 -0.057768 -0.028487 -0.006583 0.635 0.648 0.646 0.639 0.648 0.647 1.29e-03 -0.08062 3.62 -2.2618 0.00182 1
size 2.41e-02 0.006139 0.024879 0.045293 0.595 0.642 0.646 0.595 0.643 0.647 1.60e-03 0.00156 3.41 0.0425 -0.00392 1
size_grade -2.92e-03 -0.010154 -0.003138 0.003662 0.598 0.643 0.646 0.599 0.644 0.647 3.72e-04 -0.06105 2.33 -1.6745 -0.00304 1
age_nodes 5.58e-05 -0.000761 0.000107 0.000826 0.626 0.645 0.646 0.630 0.645 0.647 5.06e-05 0.06168 2.10 1.6845 -0.00140 1

Cox Model Performance

Here we evaluate the model using the RRPlot() function.

The evaluation of the raw Cox model with RRPlot()

Here we will use the predicted event probability assuming a baseline hazard for events withing 5 years

timeinterval <- 5 # Five years

h0 <- sum(dataBrestCancerTrain$status & dataBrestCancerTrain$time <= timeinterval)
h0 <- h0/sum((dataBrestCancerTrain$time > timeinterval) | (dataBrestCancerTrain$status==1))

pander::pander(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")
Initial Parameters
h0 timeinterval
0.429 5 
index <- predict(ml,dataBrestCancerTrain)
rdata <- cbind(dataBrestCancerTrain$status,ppoisGzero(index,h0))

rrAnalysisTrain <- RRPlot(rdata,atProb=c(0.90,0.80),
                     timetoEvent=dataBrestCancerTrain$time,
                     title="Train: Breast Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

ROC: Train: Breast Cancer

As we can see the Observed probability as well as the Time vs. Events are not calibrated.

Uncalibrated Performance Report

pander::pander(t(rrAnalysisTrain$OERatio),caption="O/E Ratio")
O/E Ratio
est lower upper
1.12 1.07 1.18 
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Ratio")
O/E Ratio
mean 50% 2.5% 97.5%
1.12 1.12 1.11 1.12 
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Ratio")
O/Acum Ratio
mean 50% 2.5% 97.5%
1.32 1.32 1.31 1.32 
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.677 0.677 0.664 0.691 
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.695 0.677 0.714 
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.304 0.281 0.328 
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.9 0.883 0.915 
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90% 80%
0.474 0.397 
pander::pander(t(rrAnalysisTrain$RR_atP),caption="Risk Ratio")
Risk Ratio
est lower upper
1.71 1.6 1.82 
pander::pander(rrAnalysisTrain$sufdif,caption="Logrank test")
Logrank test Chisq = 474.669311 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 1983 812 1143 96.0 393.7
class=1 390 244 175 27.1 30.7
class=2 609 462 200 344.4 401.7

Cox Calibration

op <- par(no.readonly = TRUE)


calprob <- CoxRiskCalibration(ml,dataBrestCancerTrain,"status","time")


pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")
h0 Gain DeltaTime
0.693 1.34 7.02

The RRplot() of the calibrated model

h0 <- calprob$h0
timeinterval <- calprob$timeInterval;

rdata <- cbind(dataBrestCancerTrain$status,calprob$prob)


rrAnalysisTrain <- RRPlot(rdata,atProb=c(0.90,0.80),
                     timetoEvent=dataBrestCancerTrain$time,
                     title="Cal. Train: Breast Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

ROC: Cal. Train: Breast Cancer

Calibrated Train Performance

pander::pander(t(rrAnalysisTrain$OERatio),caption="O/E Ratio")
O/E Ratio
est lower upper
1.01 0.958 1.06 
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Ratio")
O/E Ratio
mean 50% 2.5% 97.5%
0.977 0.977 0.969 0.985 
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Ratio")
O/Acum Ratio
mean 50% 2.5% 97.5%
1 1 0.999 1 
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.677 0.677 0.664 0.69 
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.695 0.677 0.714 
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.304 0.281 0.328 
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.9 0.883 0.915 
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90% 80%
0.646 0.558 
pander::pander(t(rrAnalysisTrain$RR_atP),caption="Risk Ratio")
Risk Ratio
est lower upper
1.71 1.6 1.82 
pander::pander(rrAnalysisTrain$sufdif,caption="Logrank test")
Logrank test Chisq = 474.669311 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 1983 812 1143 96.0 393.7
class=1 390 244 175 27.1 30.7
class=2 609 462 200 344.4 401.7

Performance on the external data set

index <- predict(ml,dataBrestCancerTest)
pp <- predictionStats_binary(cbind(dataBrestCancerTest$status,index),plotname="Breast Cancer")

Breast Cancer 

par(op)


prob <- ppoisGzero(index,h0)
rdata <- cbind(dataBrestCancerTest$status,prob)
rrAnalysis <- RRPlot(rdata,atThr=rrAnalysisTrain$thr_atP,
                     timetoEvent=dataBrestCancerTest$time,
                     title="Test: Breast Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

ROC: Test: Breast Cancer 

par(op)

External Data Report

pander::pander(t(rrAnalysis$OERatio),caption="O/E Ratio")
O/E Ratio
est lower upper
1.3 1.16 1.46 
pander::pander(rrAnalysis$c.index,caption="C. Index")

  • C Index: 0.663

  • Dxy: 0.326

  • S.D.: 0.0311

  • n: 686

  • missing: 0

  • uncensored: 299

  • Relevant Pairs: 266144

  • Concordant: 176497

  • Uncertain: 203702

  • cstatCI:

    mean.C Index median lower upper
    0.663 0.664 0.633 0.693 
pander::pander(t(rrAnalysis$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.66 0.619 0.701 
pander::pander((rrAnalysis$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.371 0.316 0.429 
pander::pander((rrAnalysis$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.84 0.799 0.875 
pander::pander(t(rrAnalysis$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90% 80%
0.646 0.558 
pander::pander(t(rrAnalysis$RR_atP),caption="Risk Ratio")
Risk Ratio
est lower upper
1.76 1.51 2.07 
pander::pander(rrAnalysis$sufdif,caption="Logrank test")
Logrank test Chisq = 74.963210 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 433 152 210.6 16.315 55.983
class=1 80 36 33.4 0.203 0.229
class=2 173 111 55.0 57.067 71.183

Calibrating the index on the test data

calprob <- CoxRiskCalibration(ml,dataBrestCancerTest,"status","time")

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")
h0 Gain DeltaTime
0.517 0.893 5 
rdata <- cbind(dataBrestCancerTest$status,calprob$prob)

rrAnalysis <- RRPlot(rdata,atProb=c(0.90,0.80),
                     timetoEvent=dataBrestCancerTest$time,
                     title="Cal. Test: Breast Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=calprob$timeInterval)

ROC: Cal. Test: Breast Cancer

After Calibration Report

pander::pander(t(rrAnalysis$OERatio),caption="O/E Ratio")
O/E Ratio
est lower upper
1.25 1.12 1.4 
pander::pander(rrAnalysis$c.index,caption="C. Index")

  • C Index: 0.663

  • Dxy: 0.326

  • S.D.: 0.0311

  • n: 686

  • missing: 0

  • uncensored: 299

  • Relevant Pairs: 266144

  • Concordant: 176497

  • Uncertain: 203702

  • cstatCI:

    mean.C Index median lower upper
    0.663 0.663 0.635 0.693 
pander::pander(t(rrAnalysis$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.66 0.619 0.701 
pander::pander((rrAnalysis$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.284 0.234 0.339 
pander::pander((rrAnalysis$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.899 0.865 0.927 
pander::pander(t(rrAnalysis$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90% 80%
0.614 0.506 
pander::pander(t(rrAnalysis$RR_atP),caption="Risk Ratio")
Risk Ratio
est lower upper
1.8 1.54 2.11 
pander::pander(rrAnalysis$sufdif,caption="Logrank test")
Logrank test Chisq = 80.872386 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 486 177 234.2 13.95 65.72
class=1 76 37 27.9 2.98 3.31
class=2 124 85 37.0 62.43 72.18

Logistic Model

Here we train a logistic model on the same data set

## Only label subjects that present event withing five years

dataBrestCancerR <- subset(dataBrestCancerTrain, time>=5 | status==1)
dataBrestCancerR$status <- dataBrestCancerR$status * (dataBrestCancerR$time < 5)
dataBrestCancerR$time <- NULL

#ml <- BSWiMS.model(status~1,data=dataBrestCancerR,loops=20,NumberofRepeats = 5)
ml <- BSWiMS.model(status~1,data=dataBrestCancerR,loops=0)
sm <- summary(ml)
pander::pander(sm$coefficients)
  Estimate lower mean upper u.Accuracy r.Accuracy full.Accuracy u.AUC r.AUC full.AUC IDI NRI z.IDI z.NRI Delta.AUC Frequency
grade_nodes 0.061410 0.050501 0.063719 0.080621 0.682 0.637 0.686 0.649 0.624 0.655 0.06580 0.549 13.66 15.65 -0.03109 1
size_grade 0.007168 0.004994 0.007197 0.009731 0.632 0.682 0.686 0.626 0.646 0.655 0.01787 0.294 6.74 7.73 -0.00865 1
nodes_hormon -0.056681 -0.117413 -0.059733 -0.009560 0.587 0.688 0.686 0.526 0.658 0.655 0.00280 0.455 3.44 12.15 0.00285 1
pgr -0.000438 -0.000779 -0.000441 -0.000142 0.571 0.689 0.686 0.500 0.659 0.655 0.00257 0.198 2.64 5.75 0.00412 1

Logistic Model Performance

op <- par(no.readonly = TRUE)


cprob <- predict(ml,dataBrestCancerTrain)

rdata <- cbind(dataBrestCancerTrain$status,cprob)
rrAnalysisTrain <- RRPlot(rdata,atProb=c(0.90,0.80),
                     timetoEvent=dataBrestCancerTrain$time,
                     title="Logistic Train: Breast Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=5.0)

ROC: Logistic Train: Breast Cancer 

par(op)

Training Report

pander::pander(t(rrAnalysisTrain$OERatio),caption="O/E Ratio")
O/E Ratio
est lower upper
0.965 0.917 1.01 
pander::pander(rrAnalysisTrain$c.index,caption="C. Index")

  • C Index: 0.676

  • Dxy: 0.353

  • S.D.: 0.0141

  • n: 2982

  • missing: 0

  • uncensored: 1518

  • Relevant Pairs: 6184528

  • Concordant: 4183317

  • Uncertain: 2703838

  • cstatCI:

    mean.C Index median lower upper
    0.676 0.676 0.663 0.688 
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.687 0.668 0.706 
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.323 0.3 0.348 
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.9 0.883 0.915 
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90% 80%
0.551 0.438 
pander::pander(t(rrAnalysisTrain$RR_atP),caption="Risk Ratio")
Risk Ratio
est lower upper
1.76 1.65 1.87 
pander::pander(rrAnalysisTrain$sufdif,caption="Logrank test")
Logrank test Chisq = 544.494088 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 1971 800 1145 103.8 428.3
class=1 373 227 171 18.1 20.5
class=2 638 491 202 413.1 483.7

Results on the validation set using Logistic model

pre <- predict(ml,dataBrestCancerTest)
rdata <- cbind(dataBrestCancerTest$status,pre)

rrAnalysis <- RRPlot(rdata,atThr=rrAnalysisTrain$thr_atP,
                     timetoEvent=dataBrestCancerTest$time,
                     title="Logistic Test: Breast Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=5)

ROC: Logistic Test: Breast Cancer 

par(op)

Validation Report

pander::pander(t(rrAnalysis$OERatio),caption="O/E Ratio")
O/E Ratio
est lower upper
1.22 1.08 1.36 
pander::pander(rrAnalysis$c.index,caption="C. Index")

  • C Index: 0.681

  • Dxy: 0.361

  • S.D.: 0.0304

  • n: 686

  • missing: 0

  • uncensored: 299

  • Relevant Pairs: 266144

  • Concordant: 181160

  • Uncertain: 203702

  • cstatCI:

    mean.C Index median lower upper
    0.681 0.682 0.651 0.712 
pander::pander(t(rrAnalysis$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.677 0.638 0.717 
pander::pander((rrAnalysis$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.331 0.278 0.388 
pander::pander((rrAnalysis$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.884 0.848 0.914 
pander::pander(t(rrAnalysis$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90% 80%
0.551 0.438 
pander::pander(t(rrAnalysis$RR_atP),caption="Risk Ratio")
Risk Ratio
est lower upper
1.89 1.62 2.2 
pander::pander(rrAnalysis$sufdif,caption="Logrank test")
Logrank test Chisq = 101.719564 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 427 147 211.4 19.6 68.2
class=1 115 53 45.9 1.1 1.3
class=2 144 99 41.7 78.9 93.3

Logistic Model Poisson Calibration

riskdata <- cbind(dataBrestCancerTrain$status,predict(ml,dataBrestCancerTrain),dataBrestCancerTrain$time)
calprob <- CalibrationProbPoissonRisk(riskdata,trim=0.15)

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Logistic Calibration Parameters")
h0 Gain DeltaTime
0.677 1.31 7.02 
timeinterval <- calprob$timeInterval;
gain <- calprob$hazardGain

rdata <- cbind(dataBrestCancerTrain$status,calprob$prob)


rrAnalysisTrain <- RRPlot(rdata,atProb=c(0.90,0.80),
                     timetoEvent=dataBrestCancerTrain$time,
                     title="Cal. Logistic Train: Breast Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

ROC: Cal. Logistic Train: Breast Cancer 

par(op)

Report of the calibrated logistic: training

pander::pander(t(rrAnalysisTrain$OERatio),caption="O/E Ratio")
O/E Ratio
est lower upper
1.02 0.968 1.07 
pander::pander(rrAnalysisTrain$c.index,caption="C. Index")

  • C Index: 0.676

  • Dxy: 0.353

  • S.D.: 0.0141

  • n: 2982

  • missing: 0

  • uncensored: 1518

  • Relevant Pairs: 6184528

  • Concordant: 4183317

  • Uncertain: 2703838

  • cstatCI:

    mean.C Index median lower upper
    0.676 0.677 0.663 0.69 
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.687 0.668 0.706 
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.323 0.3 0.348 
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.9 0.883 0.915 
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90% 80%
0.649 0.529 
pander::pander(t(rrAnalysisTrain$RR_atP),caption="Risk Ratio")
Risk Ratio
est lower upper
1.76 1.65 1.87 
pander::pander(rrAnalysisTrain$sufdif,caption="Logrank test")
Logrank test Chisq = 544.494088 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 1971 800 1145 103.8 428.3
class=1 373 227 171 18.1 20.5
class=2 638 491 202 413.1 483.7 
probLog <- predict(ml,dataBrestCancerTest)
aprob <- adjustProb(probLog,gain)

rdata <- cbind(dataBrestCancerTest$status,aprob)
rrAnalysis <- RRPlot(rdata,atThr=rrAnalysisTrain$thr_atP,
                     timetoEvent=dataBrestCancerTest$time,
                     title="Cal. Logistic Test: Breast Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

ROC: Cal. Logistic Test: Breast Cancer

Report of the calibrated validation

pander::pander(t(rrAnalysis$OERatio),caption="O/E Ratio")
O/E Ratio
est lower upper
1.3 1.16 1.45 
pander::pander(rrAnalysis$c.index,caption="C. Index")

  • C Index: 0.681

  • Dxy: 0.361

  • S.D.: 0.0304

  • n: 686

  • missing: 0

  • uncensored: 299

  • Relevant Pairs: 266144

  • Concordant: 181160

  • Uncertain: 203702

  • cstatCI:

    mean.C Index median lower upper
    0.681 0.681 0.651 0.711 
pander::pander(t(rrAnalysis$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.677 0.638 0.717 
pander::pander((rrAnalysis$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.331 0.278 0.388 
pander::pander((rrAnalysis$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.884 0.848 0.914 
pander::pander(t(rrAnalysis$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90% 80%
0.649 0.529 
pander::pander(t(rrAnalysis$RR_atP),caption="Risk Ratio")
Risk Ratio
est lower upper
1.89 1.62 2.2 
pander::pander(rrAnalysis$sufdif,caption="Logrank test")
Logrank test Chisq = 101.719564 on 2 degrees of freedom, p = 0.000000
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 427 147 211.4 19.6 68.2
class=1 115 53 45.9 1.1 1.3
class=2 144 99 41.7 78.9 93.3